Recognition and analysis system and method for recessive degradation of flexible conductive composite material

Abstract

The invention relates to the technical field of material detection, and discloses an identification analysis and method for recessive degradation of a flexible conductive composite material, and the system comprises a perturbation excitation module, a response collection module, a time sequence feature extraction module, a micro-response trend consistency analysis module, and a state evaluation and early warning module. Multiple periods of small-signal capacitance excitation are applied to the material, pi-pi stacking slippage and interface polarization behaviors in a conductive network are excited, electrical response data are collected to construct a time sequence feature vector, and the deviation degree of the evolution trend is further calculated. And based on the trend deviation index, performing health state scoring and triggering early warning, thereby realizing early recognition and evaluation of material recessive degradation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of material detection, in particular to a recognition analysis system and method for hidden degradation of flexible conductive composite materials. BACKGROUND

[0002] With the development of flexible electronic devices such as wearable devices, flexible displays and smart electronic skins, conductive polymer composites are widely used as key functional unit materials. Such materials usually use conductive polymers such as PEDOT:PSS, polyaniline (PANI), polypyrrole (PPy), and fillers such as carbon nanotubes, graphene or metal nanoparticles to form flexible conductive films or conductive fiber networks.

[0003] Such materials need to withstand frequent bending, stretching and deformation during use, and their conductive properties depend on π-π stacking, interfacial polarization effect and filler network continuity. Under long-term service, although there is no macroscopic crack in the material, hidden degradation processes such as conductive network segment slippage, filler decoupling and polarization path drift often occur, resulting in performance degradation such as conductive property decline and response delay.

[0004] Existing detection techniques mostly rely on resistance changes, morphology monitoring or destructive tensile tests, making it difficult to identify the above-mentioned hidden degradation behavior early. Therefore, there is an urgent need for a hidden degradation recognition system specifically for conductive polymer composites in flexible electronic devices, which can monitor the evolution trend of the conductive structure through non-destructive perturbation excitation, and realize early warning and life assessment. SUMMARY

[0005] The purpose of the present application is to provide a recognition analysis system for hidden degradation of flexible conductive composites, which has a non-destructive, real-time, trend-oriented hidden degradation recognition mechanism, breaking through the technical limitations of traditional monitoring methods in identifying "invisible damage".

[0006] The above technical purpose of the present application is achieved by the following technical solution:

[0007] The recognition analysis system for hidden degradation of flexible conductive composites comprises:

[0008] The perturbation excitation module is used to apply small signal capacitance excitation of a predetermined frequency and amplitude to the flexible conductive polymer composite material in multiple excitation cycles, to excite π-π stacking slippage behavior and interfacial polarization response in the conductive network of the material, thereby causing the material's electrical performance evolution process, which is reflected by changes in electrical response;

[0009] The response acquisition module is configured to acquire electrical response data generated by the change of the electrical response of the material to each excitation cycle, and the electrical response data includes complex impedance spectrum, dielectric constant change and electrical response hysteresis parameter, and is used to represent the real-time state of the electrical performance evolution process.

[0010] The time sequence feature extraction module is configured to construct a time sequence feature vector of the electrical performance evolution based on the electrical response data of the multiple excitation cycles, and the time sequence feature vector is used to describe the trend evolution characteristics of the electrical conduction path stability and the polarization behavior changing with the cycle.

[0011] The micro-response trend consistency analysis module is configured to perform inter-cycle trend consistency calculation on the time sequence feature vector to generate a trend deviation index used to describe the fluctuation degree of the electrical performance evolution trend, and the trend deviation index is used to represent the implicit degradation degree of the electrical conduction structure of the material.

[0012] The state evaluation and early warning module is configured to output a health state score of the material according to the trend deviation index, and generate corresponding early warning information according to a preset score level and a response rule.

[0013] Further, the response acquisition module comprises:

[0014] The cycle response sampling unit is configured to sample the phase difference between the voltage and the current in real time at multiple time points in each excitation cycle to construct a phase response trajectory in the cycle.

[0015] The dynamic gain self-adjusting submodule is configured to dynamically adjust the gain setting of the signal acquisition channel according to the change trend of the electrical response amplitude of the material, so as to adapt to the sampling demand of the response amplitude change.

[0016] Further, the time sequence feature extraction module comprises:

[0017] The feature component normalization submodule is configured to perform amplitude normalization and cycle smoothing processing on the input electrical response features and structural deformation features, respectively.

[0018] The feature evolution rate calculation submodule is configured to calculate the incremental change rate of each normalized feature between consecutive excitation cycles, so as to represent the change intensity of the response trend.

[0019] The feature fusion coding unit is configured to combine each feature change rate into a unified time sequence feature vector according to a preset coding rule, so as to be input into the subsequent trend consistency analysis module.

[0020] Further, the feature fusion encoding unit encodes the period position encoding and a lag response factor, wherein the period position encoding is used to reserve the time sequence of each excitation period in the overall sequence, and the lag response factor is used to weight the influence degree of recent periods on the trend change.

[0021] Further, the time sequence feature vector is mapped to a preset trend classification space, and the classification space includes three sub-classes of growth trend, stable trend and oscillation trend, to assist in judging the dynamic mode type of the material electrical performance evolution.

[0022] Further, the micro-response trend consistency analysis module includes:

[0023] The time sequence trend comparison sub-module is used to extract a plurality of continuous sub-sequences from the fusion feature vector sequence in a sliding window manner, and construct corresponding electrical response trend vectors and structural response trend vectors in each sliding window;

[0024] The trend consistency measurement sub-module is used to calculate a trend consistency score S based on the direction angle and the trend slope difference between the electrical response trend vector and the structural response trend vector. 一致 The trend consistency score result can be used as a trend deviation degree index to judge whether the evolution trends of the electrical and structural responses in the sliding window are consistent, and the score calculation formula is as follows:

[0025] S 一致 = exp(-α·0)·exp(-β·|slope 电 -slope 形 |)

[0026] Wherein,

[0027] θ represents the angle between the electrical trend vector and the structural trend vector in the current window, and is defined as:

[0028]

[0029] slope 电 represents the linear fitting slope of the electrical trend vector;

[0030] slope 形 represents the linear fitting slope of the structural trend vector;

[0031] represents the electrical response / structural response trend vector in the current window;

[0032] α, β represent the preset adjustment factors of the system, which are used to adjust the weights of the direction consistency and the slope consistency on the score, respectively;

[0033] A threshold adaptive determination unit dynamically determines a trend consistency score threshold T according to the material type, the response data historical fluctuation amplitude, and the known degradation labeled sample 一致 When the score S 一致 <T 一致 , it is determined that the window has a response mismatch trend, indicating that the material may have hidden degradation or interface instability risk.

[0034] Further settings, the state evaluation and early warning module includes:

[0035] A health score generation unit is configured to construct a state evaluation function based on the trend deviation index of each sliding window, the perturbation response strength, and other parameters, and output a health state score of the current material under the excitation interval;

[0036] A graded response decision unit is configured to perform grade classification according to the health state score and a plurality of preset risk response thresholds, and trigger corresponding early warning strategies;

[0037] The early warning strategies include recording labeled samples, sending external alarm signals, adjusting perturbation excitation parameters, and prompting manual maintenance suggestions.

[0038] Further settings, the state evaluation function is:

[0039] H=w1·(1-S 一致 )+w2·ΔA+w3·Δf

[0040] Wherein,

[0041] H represents the health state score (value range [0, 1]), the larger the better, indicating that it is closer to degradation instability;

[0042] ΔA represents the perturbation response amplitude change, indicating the degree of drift of the electrical response amplitude to the excitation period;

[0043] Δf represents the electrical response main frequency offset, reflecting the change of the material internal interface response;

[0044] w1, w2, w3 represent weighting coefficients, satisfying w1+w2+w3=1, used to balance the contribution of each feature to the total score.

[0045] Further settings, the graded response decision unit divides the health state score into three intervals:

[0046] Normal interval [0, T1]: no alarm is triggered;

[0047] Early warning interval (T1, T2]: record data and send an early warning signal;

[0048] Dangerous interval (T2, 1]: activate the perturbation excitation parameter adjustment logic and output manual intervention suggestions.

[0049] Wherein, T1, T2 are experience setting or training obtained score threshold, satisfy 0<T1<T2<1.

[0050] Another object of the present application is to provide a method for identifying and analyzing the hidden degradation of conductive composite materials in flexible electronic devices, comprising the following steps:

[0051] The perturbation excitation application step: a plurality of periodic small signal capacitance excitations are applied to the flexible conductive polymer composite material, which is used to stimulate the π-π stacking slip behavior and interface polarization response in the conductive network of the material, and to induce the evolution of the electrical performance;

[0052] The response data acquisition step: the electrical response data of the material under each excitation cycle is collected, including the complex impedance spectrum, the dielectric constant change and the electrical response hysteresis parameter;

[0053] The time sequence feature construction step: the time sequence feature vector is constructed based on the electrical response data, which is used to describe the evolution trend of the electrical performance of the material with the change of the excitation cycle;

[0054] The trend consistency identification step: the trend consistency analysis is performed on the time sequence feature vector in a sliding window manner, and a trend deviation index is generated to represent the fluctuation degree of the evolution trend;

[0055] The health state evaluation step: a health score function is constructed according to the trend deviation index, the electrical response amplitude change and the main frequency shift, and a health state score is output;

[0056] The early warning information generation step: the health state score is compared with the preset score level, and the early warning information corresponding to the level is generated.

[0057] In summary, the present application has the following beneficial effects:

[0058] By applying a plurality of periodic small signal capacitance excitations to the flexible conductive polymer composite material, the π-π stacking slip behavior and interface polarization response in the conductive network of the material can be induced without destroying the material structure, the electrical excitation of the subtle degradation characteristics of the material is realized, and the detection sensitivity of the hidden defect state is effectively improved;

[0059] The electrical response data under each excitation cycle is collected in real time by the response acquisition module, including the complex impedance spectrum, the dielectric constant change and the electrical response hysteresis parameter, so that the system can record the whole process of the electrical performance evolution, and provide high time resolution basic data for subsequent trend extraction and consistency analysis;

[0060] The time sequence feature extraction module converts the electrical response data of multiple periods into a time sequence feature vector, which can accurately reflect the time evolution trend of the conductive path stability and polarization behavior, thereby constructing a response feature model with evolution trend recognition capability;

[0061] The micro-response trend consistency analysis module performs trend consistency analysis on the time sequence feature vector, quantifies the trend fluctuation degree of the evolution process, generates a trend deviation index, effectively identifies the small abnormal changes and discontinuous behavior in the response process, and is used to determine whether there is a structural hidden danger;

[0062] The state evaluation and early warning module scores the health state based on the trend deviation index, generates early warning information in combination with the score level and the response rule, realizes early state recognition and response triggering mechanism of the conductive polymer composite material in the implicit deterioration stage, and provides a reliable failure prevention and service life extension strategy for flexible electronic devices.

[0063] The modules of the present application form a closed loop link from "excitation-response-evolution modeling-trend analysis-risk early warning", which is different from the existing passive detection mode through deterioration consequences, realizes active identification of early characteristics of conductive performance evolution, trend modeling and intelligent early warning output, and is especially suitable for monitoring scenes of early invisible defects. BRIEF DESCRIPTION OF DRAWINGS

[0064] Figure 1 It is a system architecture schematic diagram of the embodiment. DETAILED DESCRIPTION

[0065] The present application will be further described in detail below in combination with the drawings.

[0066] Embodiment:

[0067] The identification analysis system for implicit deterioration of flexible conductive composite material, as shown in Figure 1 , comprises:

[0068] The perturbation excitation module is used for applying small signal capacitance excitation of a preset frequency and amplitude to the flexible conductive polymer composite material for multiple excitation periods, so as to excite the π-π stacking slip behavior and interface polarization response in the conductive network of the material, thereby causing the electrical performance evolution process of the material, and the electrical performance evolution process is embodied by electrical response change;

[0069] The "π-π stacking slip behavior" is that the π electrons containing aromatic structure in the conductive polymer chain (such as PEDOT or PANI) slightly translate or decouple under the perturbation excitation, so as to cause the change of conductive path length, thereby causing the fluctuation of resistance and phase response;

[0070] The "interfacial polarization response" refers to the charge accumulation phenomenon formed at the interface between the conductive filler and the polymer matrix in the composite material, which is characterized by the enhancement of dielectric constant fluctuation and hysteresis, usually with certain frequency selectivity, and can be reflected in the dynamic evolution process through the capacitance response curve.

[0071] In this embodiment, the perturbation excitation module uses a function signal source to generate a small signal sinusoidal wave voltage excitation with a frequency of 10 Hz to 100 kHz, the voltage amplitude is controlled within 0.5 V, the current is less than 10 μA, and the excitation signal is ensured not to cause material structure damage or significant nonlinear response, and the number of excitation cycles is set to 30 cycles; the duration of each cycle is 50 ms, and the excitation interval time is 100 ms; the excitation scheme aims to induce the response of the micro-conductive structure in the material and form a detectable electrical performance evolution trend.

[0072] The response acquisition module is configured to acquire electrical response data generated by the electrical response change of the material to each excitation cycle, and the electrical response data includes complex impedance spectrum, dielectric constant change and electrical response hysteresis parameter, and is used to represent the real-time state of the electrical performance evolution process.

[0073] The time sequence feature extraction module is configured to construct a time sequence feature vector of the electrical performance evolution based on the electrical response data of multiple excitation cycles, and the time sequence feature vector is used to describe the trend evolution characteristics of the stability of the conductive path and the polarization behavior with the change of the cycle.

[0074] The micro-response trend consistency analysis module is configured to perform cycle-to-cycle trend consistency calculation on the time sequence feature vector to generate a trend deviation index used to describe the fluctuation degree of the electrical performance evolution trend, and the trend deviation index is used to represent the implicit degradation degree of the conductive structure of the material.

[0075] The state evaluation and early warning module is configured to output a health state score of the material according to the trend deviation index, and generate corresponding early warning information according to a preset score level and response rule.

[0076] In this embodiment, PEDOT:PSS and carbon nanotube composite conductive film, which is a representative of flexible conductive material, is selected, which has good flexible deformation ability and stable π-π stacking electron path structure, and is widely used in flexible sensors, wearable electronic fabrics and other devices.

[0077] In this embodiment, the response acquisition module is used to acquire the response characteristics of the conductive polymer composite material used in the flexible electronic device under the action of periodic excitation with high precision, so as to support the subsequent time sequence feature extraction and degradation identification analysis.

[0078] The response acquisition module comprises a cycle response sampling unit, a dynamic gain self-adjusting sub-module and an auxiliary structure displacement sensing unit.

[0079] The periodic response sampling unit is set to work synchronously with the perturbation excitation module, which receives the excitation period signal and collects the instantaneous phase difference between the voltage and current of the conductive polymer composite at multiple time points within each excitation period. The phase difference data is used to construct the complete phase response trajectory within the period.

[0080] In this embodiment, the sampling frequency is set to 100 kHz, and no less than 512 points are collected per period to ensure the capture of the details of the hysteresis behavior and response curve.

[0081] The collected results are used to reveal the nonlinear evolution characteristics in the electrical response of the material, such as hysteresis area change, response speed fluctuation, and dielectric shift.

[0082] Considering that the electrical response amplitude of the material may decay over time during the degradation process, to avoid the decrease in signal-to-noise ratio due to the weakening of the signal strength, a dynamic gain self-adjusting sub-module is set, which has a built-in feedback control circuit. According to the variation trend of the collected original response amplitude, the gain of the pre-amplifier (range 10x to 1000x) is automatically adjusted to keep the electrical response data within the optimal input dynamic range of the ADC (analog-to-digital converter).

[0083] If the response amplitude decreases by more than 10% in the next 3 periods, the gain is automatically increased by one level, and the gain parameter is updated in real time. The dynamic gain self-adjusting sub-module enhances the system's ability to distinguish weak response signals in the early degradation state, ensuring data continuity and effectiveness.

[0084] To construct the coupling response characteristics, a micro deformation sensor (such as a flexible strain gauge or MEMS capacitance sensing unit) is placed on the surface of the material in this embodiment to collect the material micro deformation during the excitation period.

[0085] The micro deformation signal sampling frequency is synchronized with the periodic response sampling unit; the collected deformation data and the phase response trajectory are jointly input into the time series feature extraction module to generate an electrical-structural coupling feature vector, which improves the recognition ability of local damage and structural relaxation.

[0086] In this embodiment, the time series feature extraction module is used to process the original data from the response collection module, extract the evolution trend of the electrical response and structural deformation in multiple excitation periods, and structure it into a feature vector for easy classification and discrimination.

[0087] The time series feature extraction module includes a feature component normalization sub-module, a feature evolution rate calculation sub-module, a feature fusion encoding unit, and a trend classification sub-module.

[0088] The characteristic component normalization submodule first preprocesses the input multi-period response data (including in-period phase response trajectory and structural micro-deformation variable); amplitude normalization processing is performed on each type of data respectively, mapping the numerical value to the [0, 1] interval, eliminating the scale deviation between different characteristic sources; sliding window smoothing processing (window length is generally 3-5 periods) is performed on the period sequence, which is used to eliminate high-frequency noise and highlight the trend form.

[0089] For example: the electrical response trajectory shows a downward trend in consecutive periods, and the structural deformation variable is stable, so the normalized data can clearly reflect the composite trend of “deterioration of electrical performance and maintenance of structure”.

[0090] The characteristic evolution rate calculation submodule calculates the change rate between consecutive periods based on the normalized period data,

[0091] Define any characteristic f t At the value of period t, the change rate is defined as:

[0092]

[0093] Wherein,

[0094] R t represents the change rate of the characteristic of the material in the tth excitation period;

[0095] f t represents the normalized characteristic value (which can be the electrical response amplitude, phase shift, deformation variable, etc.) collected in the tth period;

[0096] f t-1 represents the normalized characteristic value of the previous period (t-1 period);

[0097] is a small constant constant to prevent division by zero (for example, 10 -6 );

[0098] The change rates of electrical response and structural characteristics are calculated independently to form a “trend change intensity vector”, which quantifies the performance drift amplitude of the material in each period and reflects its stability or deterioration rate.

[0099] In order to enable the subsequent module to process time series characteristics, a fusion encoding mechanism is used to integrate multiple source change rates into a unified time series vector.

[0100] First, add period position coding to each period to record its position in the complete sequence;

[0101] Then add a lag weight factor to each change rate characteristic to highlight the dominant role of recent periods on the overall trend;

[0102] Finally, a unified structure is formed as follows:

[0103]

[0104] wherein,

[0105] V t denotes the fusion feature encoding vector corresponding to the t-th cycle;

[0106] Position(t) represents the time position encoding of the cycle order (which can be linear encoding or cosine position encoding);

[0107] denotes the rate of change of the electrical response feature (such as phase shift, hysteresis area, etc.);

[0108] denotes the rate of change of the structural feature (such as micro deformation);

[0109] λ1, λ2 are preset weights, and the position encoding can adopt linear or cosine function encoding method.

[0110] The structure realizes the fusion of time information and trend intensity, and has strong trend recognition ability.

[0111] The fused time sequence feature vector sequence {V1, V2,..., V T} is subjected to trend pattern recognition and mapped to the following three types of trend spaces:

[0112] Growing trend: both electrical and structural indicators show continuous rise;

[0113] Stable trend: the feature change rate fluctuates little and is overall stable;

[0114] Oscillating trend: the feature change rate periodically reverses and exists periodic disturbance.

[0115] The classification criteria are determined in advance through test labeled data, and different material degradation behaviors correspond to different trends. For example, the oscillating type usually indicates that the material interface is unstable or is affected by environmental disturbance; the growing type may mean strain enhancement or interface polarization accumulation.

[0116] In the embodiment, the micro-response trend consistency analysis module is used to judge the consistency degree of the material between the electrical response trend and the structural response trend, and then identify the early performance of micro degradation, so as to avoid missed detection due to deviation of the response mode.

[0117] The module mainly includes: a time sequence trend comparison submodule, a trend consistency measurement submodule, and a threshold adaptive judgment unit.

[0118] The time trend comparison submodule extracts multiple fixed-length subsequences from the fusion feature vector sequence in a sliding window manner. For example, a window length of 10 cycles and a sliding step of 2 are selected.

[0119] In each sliding window, the following are constructed:

[0120] The electrical response trend vector (composed of adjacent cycle electrical parameter change rates);

[0121] The structural response trend vector (generated from the micro-deformation sequence);

[0122] The trend consistency measure submodule calculates the angle and slope of the two trend vectors, and calculates the trend consistency score,

[0123] Trend angle (direction similarity):

[0124] Calculate the angle between the trend vectors:

[0125]

[0126] The closer θ is to 0, the more consistent the direction of the two trends is.

[0127] Rate difference (trend intensity matching degree):

[0128] Linear fitting is performed on respectively to obtain the slope, and the difference between the two is calculated.

[0129] Trend consistency score:

[0130] The final score is as follows:

[0131] S 一致 = exp(-α·θ)·exp(-β·|slope 电 -slope 形 |)

[0132] Where:

[0133] α = 1.5, β = 2.0, are empirical adjustment parameters;

[0134] If θ = 10° ≈ 0.1745 and the slope difference is 0.05, then: 一致 ≈ 0.77

[0135] To avoid false judgments of fixed thresholds, the system introduces an adaptive mechanism:

[0136] For different material types, set the initial score threshold range (such as [0.60, 0.80]);

[0137] Based on the score of the window appearing the degradation label in the training data, the average distribution is counted to form a dynamic score threshold T 一致 ;

[0138] In actual discrimination, if S 一致 <T 一致 , output the "trend mismatch" signal, and record as a potential risk section.

[0139] Through the synergistic scoring of the trend vector angle and the change rate, and the introduction of a threshold adaptive mechanism based on material characteristics, this module can realize the detailed classification of "electrical disorder-structural maintenance" or "synchronous degradation" and other situations in the evolution process of material performance, which helps to improve the recognition accuracy and robustness of the system in the micro-response stage degradation.

[0140] In this embodiment, the state evaluation and early warning module is used to comprehensively evaluate the electrical response trend, structural deformation amplitude and frequency stability of the material after the perturbation excitation test is completed, and to score the current state of the material, and to determine whether to trigger different levels of early warning actions.

[0141] The state evaluation and early warning module comprises:

[0142] A health score generation unit is configured to construct a state evaluation function based on the trend deviation index of each sliding window, the perturbation response strength and other parameters, and output the health state score of the current material under the excitation interval.

[0143] A graded response decision unit is configured to perform level classification according to the health state score and the preset multi-level risk response threshold, and trigger the corresponding early warning strategy.

[0144] The early warning strategy includes recording the labeled sample, sending an external alarm signal, adjusting the perturbation excitation parameters, and prompting the manual maintenance suggestion.

[0145] The state evaluation function is:

[0146] H=w1·(1-S 一致 )+w2·ΔA+w3·Δf

[0147] Wherein,

[0148] H represents the health state score (value range [0, 1]), the larger the better, indicating the closer to the degradation instability;

[0149] ΔA represents the perturbation response amplitude change, indicating the drift degree of the electrical response amplitude to the excitation period;

[0150] Δf represents the electrical response main frequency offset, reflecting the change of the material internal interface response;

[0151] w1, w2, w3 represent weighting coefficients, satisfying w1+w2+w3=1, which are used to balance the contribution of each feature to the total score;

[0152] w1: used to indicate the stability of the measurement trend, which is the main indicator of evolutionary consistency. It is most sensitive to early latent degradation and can be set as the main weight, such as 0.5;

[0153] w2: Electrical response fluctuation amplitude, often used to identify sudden defects, has a small contribution in the stable stage, so w3 can be set as a secondary weight, such as 0.3;

[0154] w3: The main frequency offset reflects the change of polarization structure or network path damage. It changes relatively slowly and can be set as an auxiliary weight, such as 0.2;

[0155] The hierarchical response decision unit divides the health status score into three levels:

[0156] Normal interval [0, T1]: no warning is triggered;

[0157] Warning interval (T1, T2]: record data and send warning signals;

[0158] Dangerous interval (T2, 1]: Activate the perturbation excitation parameter adjustment logic and output manual intervention suggestions.

[0159] Among them, T1.T2 is the score threshold value set by experience or obtained through training, and it satisfies 0 <T1<T2<1,典型设置为:T1=0.40,T2=0.75。

[0160] This embodiment provides a method for identifying and analyzing latent degradation of flexible conductive composite materials. This method combines small-signal excitation, electrical response acquisition, trend identification, and scoring analysis to achieve quantitative assessment and early warning of material degradation. Specifically, it includes:

[0161] 1. Perturbation excitation application steps

[0162] During the initial operation of flexible electronic devices or during regular maintenance, multiple cycles of a small-signal capacitive excitation signal are applied to the conductive polymer composite. The signal frequency is set at 0.1-1 Hz, with an amplitude of 10-50 mV, and the electrical stimulation is kept within the non-destructive range. This induces π-π stacking slip and interfacial polarization responses within the conductive channels within the material. These microscopic behaviors lead to slight but measurable changes in its macroscopic electrical properties, such as impedance and dielectric properties.

[0163] 2. Response data collection steps

[0164] During each excitation cycle, a high-resolution impedance analyzer is used to collect the corresponding electrical response data, including:

[0165] Complex impedance spectrum, dielectric constant change, voltage-current hysteresis loop area;

[0166] A set of response data is formed per cycle, and time series is recorded in time sequence.

[0167] 3. Time series feature construction step

[0168] For the above response data, representative indicators (such as impedance real part mean Z t , dielectric constant instantaneous value ε r，t and hysteresis area AV H，t ) are extracted per cycle, and a three-dimensional vector is constructed:

[0169] T t =[Z′ t ,ε r，t ,A VH，t ]

[0170] Vectors of multiple cycles are spliced to form a time series feature vector sequence:

[0171] {T1, T2,..., T N}

[0172] The sequence is used to identify the trend change of the electrical performance over time.

[0173] 4. Trend consistency identification step

[0174] The time series feature vector sequence is processed using a sliding window method, and the trend direction angle θ i and the slope difference Δk i between each adjacent two windows are calculated. According to this, the trend consistency score S 一致 is defined, which is used to measure the stability of the evolution of material response characteristics:

[0175]

[0176] Where:

[0177] M is the total number of sliding windows;

[0178] θ i is the angle between the current and previous window trend vectors;

[0179] Δk i is the slope difference;

[0180] cos(θ i ) measures the consistency of the trend direction;

[0181] exp(-|Δk i |) measures the smoothness of the trend rate;

[0182] The higher the score, the more consistent the response trend, and the lower the score, the more fluctuation in the trend, indicating potential degradation.

[0183] 5. Health score step

[0184] To comprehensively evaluate the degree of material degradation, the following health score function is defined:

[0185] H = W1 · (1 - S 一致 ) + w2 · ΔA + W3 · Δf

[0186] Where:

[0187] H: Health score, ranging from [0, 1];

[0188] W1 = 0.5, w2 = 0.3, w3 = 0.2;

[0189] ΔA: Change rate of electrical response amplitude, such as maximum fluctuation of dielectric constant or impedance real part;

[0190] Δf: Impedance spectrum main peak frequency shift;

[0191] 1 - S 一致 : Trend fluctuation term, the higher the score, the more unstable the trend.

[0192] This scoring function can quantitatively reflect the stability and evolution rate of the microstructure of the conductive network of the material.

[0193] 6. Early warning information generation step

[0194] The early warning threshold rule is set as follows:

[0195] When H < 0.4, the system judges the health status;

[0196] When 0.4 ≤ H < 0.75, the system issues a warning prompt;

[0197] When H ≥ 0.75, the system determines a high-risk state and outputs a maintenance suggestion or a disable signal.

[0198] Regarding the application of this method:

[0199] The method is implemented on a flexible conductive polypyrrole film sample S-02, and the electrical response data within 10 cycles is recorded. The final health score H = 0.79 is calculated, and the system determines that it is in a high-risk level. Further observation by scanning electron microscope shows that there are indeed obvious ruptures in the conductive polymer chain segments of the sample, verifying the accuracy of the early warning of this method for implicit degradation.

[0200] The above-described embodiments do not constitute a limitation on the protection scope of the technical solutions. Any modification, equivalent replacement and improvement made within the spirit and principle of the above-described embodiments should be included in the protection scope of the technical solutions.

Claims

1. A system for identifying and analyzing latent degradation of flexible conductive composite materials, characterized in that: include: A perturbation excitation module is used to apply small-signal capacitive excitation of preset frequency and amplitude to the flexible conductive polymer composite material over multiple excitation cycles to stimulate the π-π stacking slip behavior and interface polarization response in the conductive network of the material, thereby inducing the evolution of the material's electrical properties, which is reflected by changes in the electrical response; A response acquisition module is used to collect electrical response data generated by the electrical response changes of the material to each excitation cycle. The electrical response data includes complex impedance spectrum, dielectric constant change and electrical response hysteresis parameter, which are used to characterize the real-time state of the electrical performance evolution process; A time series feature extraction module is used to construct a time series feature vector of the electrical performance evolution based on the electrical response data of multiple excitation cycles. The time series feature vector is used to describe the trend evolution characteristics of the conductive path stability and polarization behavior with periodic changes; a micro-response trend consistency analysis module, configured to perform inter-cycle trend consistency calculation on the time series feature vector and generate a trend deviation index for describing the degree of fluctuation in the electrical performance evolution trend, wherein the trend deviation index is used to characterize the degree of implicit degradation of the conductive structure of the material; The status assessment and early warning module is used to output the current health status score of the material according to the trend deviation index, and generate corresponding early warning information according to the preset scoring level and response rules.

2. The identification and analysis system for hidden degradation of flexible conductive composite materials according to claim 1, characterized in that: The response collection module includes: A cycle response sampling unit is used to sample the phase difference between voltage and current in real time at multiple time points within each excitation cycle to construct a phase response trajectory within the cycle; The dynamic gain self-adjustment submodule is used to dynamically adjust the gain setting of the signal acquisition channel according to the changing trend of the material electrical response amplitude to adapt to the sampling requirements of the response amplitude change.

3. The identification and analysis system for hidden degradation of flexible conductive composite materials according to claim 2, characterized in that: The temporal feature extraction module includes: The feature component normalization submodule is used to perform amplitude normalization and period smoothing on the input electrical response characteristics and structural deformation characteristics respectively; The feature evolution rate calculation submodule is used to calculate the incremental change rate of each normalized feature between consecutive excitation cycles to represent the intensity of the change in the response trend; The feature fusion coding unit is used to combine the feature change rates into a unified time series feature vector according to preset coding rules for input into the subsequent trend consistency analysis module.

4. The identification and analysis system for hidden degradation of flexible conductive composite materials according to claim 3, characterized in that: The feature fusion coding unit performs coding processing based on period position coding and lag response factor, wherein the period position coding is used to retain the time order of each excitation period in the overall sequence, and the lag response factor is used to weight the influence of the recent period on the trend change.

5. The identification and analysis system for hidden degradation of flexible conductive composite materials according to claim 4, characterized in that: The time series feature vector is mapped to a preset trend classification space, which includes three subcategories: growth trend, stable trend and oscillation trend, and is used to assist in determining the dynamic mode type of the evolution of the electrical properties of the material.

6. The identification and analysis system for hidden degradation of flexible conductive composite materials according to claim 5, characterized in that: The micro-response trend consistency analysis module includes: A time series trend comparison submodule is used to extract multiple continuous subsequences from the fused feature vector sequence in a sliding window manner, and to construct corresponding electrical response trend vectors and structural response trend vectors in each sliding window; The trend consistency measurement submodule is used to calculate the trend consistency score S based on the direction angle between the electrical response trend vector and the structural response trend vector and the trend slope difference. The trend consistency score result can be used as a trend deviation index to determine whether the evolution trends of the electrical and structural responses within the sliding window are consistent. The score calculation formula is as follows: S 一致 =exp(-α·θ)·exp(-β·|slope 电 -slope 形 |) in, θ represents the angle between the electrical trend vector and the structural trend vector in the current window, which is defined as: slope 电 represents the linear fit slope of the electrical trend vector; slope 形 represents the slope of the linear fit of the structural trend vector; Represents the electrical response / structural response trend vector in the current window; α and β represent the system preset adjustment factors, which are used to adjust the weights of direction consistency and slope consistency on the score respectively; The threshold adaptive judgment unit dynamically determines the trend consistency score threshold T according to the material type, historical fluctuation range of the response data and known degradation annotation samples. 一致 , when the score S 一致 <T 一致 When , it is determined that there is a response mismatch trend in this window, indicating that the material may have the risk of hidden degradation or interface instability.

7. The identification and analysis system for hidden degradation of flexible conductive composite materials according to claim 6, characterized in that: The status assessment and early warning module includes: A health score generating unit is used to construct a state evaluation function based on multiple parameters such as the trend deviation index of each sliding window and the perturbation response strength, and output the health state score of the current material under the said excitation interval; A hierarchical response decision unit is used to classify the health status scores and the preset multi-level risk response thresholds and trigger the corresponding early warning strategy; The early warning strategy includes: recording and marking samples, sending external alarm signals, adjusting perturbation excitation parameters and prompting manual maintenance suggestions.

8. The identification and analysis system for hidden degradation of flexible conductive composite materials according to claim 7, characterized in that: The state evaluation function is: H=w1·(1-S coincidence)+w2·ΔA+w3·Δf in, H represents the health status score (value range [0, 1]), the larger the value, the closer to degradation and instability; ΔA represents the change in the perturbation response amplitude, which indicates the degree of drift of the electrical response amplitude with respect to the excitation period; Δf represents the main frequency offset of the electrical response, reflecting the change in the interface response within the material; w1, w2, and w3 represent weighting coefficients, satisfying w1+w2+w3=1, which are used to balance the contribution of each feature to the total score.

9. The identification and analysis system for hidden degradation of flexible conductive composite materials according to claim 8, characterized in that: The hierarchical response decision unit divides the health status score into three levels: Normal interval [0, T1]: no warning is triggered; Warning interval (T1, T2]: record data and send warning signals; Dangerous interval (T2, 1]: Activate the perturbation excitation parameter adjustment logic and output manual intervention suggestions; Among them, T1 and T2 are the scoring thresholds set by experience or obtained through training, and they meet the requirement of 0 <T1<T2<1。 10. A method for identifying and analyzing latent degradation of conductive composite materials in flexible electronic devices, applied to a system for identifying and analyzing latent degradation of flexible conductive composite materials according to any one of claims 1 to 9, characterized in that: The following steps are involved: Perturbation excitation application step: applying multiple cycles of small-signal capacitive excitation to the flexible conductive polymer composite material to stimulate the π-π stacking slip behavior and interface polarization response in the conductive network of the material, thereby triggering the evolution of electrical properties; Response data collection step: collecting electrical response data of the material in each excitation cycle, wherein the electrical response data includes complex impedance spectrum, dielectric constant change and electrical response hysteresis parameter; A time series feature construction step: constructing a time series feature vector based on the electrical response data to describe the evolution trend of the material's electrical properties as the excitation cycle changes; Trend consistency identification step: performing trend consistency analysis on the time series feature vector in a sliding window manner to generate a trend deviation index for characterizing the degree of fluctuation of the evolution trend; Health status assessment step: constructing a health score function based on the trend deviation index, the electrical response amplitude change and the main frequency offset, and outputting a health status score; The step of generating early warning information is to compare the health status score with a preset score level and generate early warning information of a corresponding level.

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